Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (258 pages) : , illustrations

ISBN: 0-12-803666-4 , 0-12-803636-2

Note: Front Cover -- Introduction to Nature-Inspired Optimization -- Copyright -- Contents -- About the Authors -- Preface -- Acknowledgment -- Notation -- 1 An Introduction to Optimization -- 1.1 Introduction -- 1.2 Classes of Optimization Problems -- 1.3 Using Calculus to Optimize a Function -- 1.4 A Brute Force Method! -- 1.5 Gradient Methods -- 1.6 Nature Inspired Optimization Algorithms -- 1.7 Randomness in Nature Inspired Algorithms -- 1.8 Testing Nature Inspired Algorithms -- 1.9 Summary -- 1.10 Problems -- 2 Evolutionary Algorithms -- 2.1 Introduction -- 2.2 Introduction to Genetic Algorithms -- 2.3 Alternative Methods of Coding -- 2.4 Alternative Methods of Selection for Mating -- 2.5 Alternative Forms of Mating -- 2.6 Alternative Forms of Mutation -- 2.7 Theoretical Background to GAs -- 2.8 Continuous or Decimal Coding -- 2.9 Selected Numerical Studies Using the Continuous GA -- 2.10 Some Applications of the Genetic Algorithm -- 2.11 Differential Evolution -- 2.12 Other Variants of Differential Evolution -- 2.13 Numerical Studies -- 2.14 Some Applications of Differential Evolution -- 2.15 Summary -- 2.16 Problems -- 3 Particle Swarm Optimization Algorithms -- 3.1 Origins of Particle Swarm Optimization -- 3.2 The PSO Algorithm -- 3.3 Developments of the PSO Algorithm -- 3.4 Selected Numerical Studies Using PSO -- 3.5 A Review of Some Relevant Developments -- 3.6 Some Applications of Particle Swarm Optimization -- 3.7 Summary -- 3.8 Problems -- 4 The Cuckoo Search Algorithm -- 4.1 Introduction -- 4.2 Description of the Cuckoo Search Algorithm -- 4.3 Modi cations of the Cuckoo Search Algorithm -- 4.4 Numerical Studies of the Cuckoo Search Algorithm -- 4.5 Extensions and Developments of the Cuckoo Search Algorithm -- 4.6 Some Applications of the Cuckoo Search Algorithm -- 4.7 Summary -- 4.8 Problems -- 5 The Fire y Algorithm -- 5.1 Introduction. , 5.2 Description of the Fire y Inspired Optimization Algorithm -- 5.3 Modi cations to the Fire y Algorithm -- 5.4 Selected Numerical Studies of the Fire y Algorithm -- 5.5 Developments of the Fire y Algorithm -- 5.6 Some Applications of the Fire y Algorithm -- 5.7 Summary -- 5.8 Reader Exercises -- 6 Bacterial Foraging Inspired Algorithm -- 6.1 Introduction -- 6.2 Description of the Bacterial Foraging Optimization Algorithm -- 6.3 Modi cations of the BFO Search Algorithm -- 6.4 Selected Numerical Studies of the BFO Search Algorithm -- 6.5 Theoretical Developments of the BFO Algorithm -- 6.6 Some Applications of the Bacterial Foraging Optimization -- 6.7 Summary -- 6.8 Problems -- 7 Arti cial Bee and Ant Colony Optimization -- 7.1 Introduction -- 7.2 The Arti cial Bee Colony Algorithm (ABC) -- 7.3 Modi cations of the Arti cial Bee Colony (ABC) Algorithm -- 7.4 Selected Numerical Studies of the Performance of the ABC Algorithm -- 7.5 Some Applications of Arti cial Bee Colony Optimization -- 7.6 Description of the Ant Colony Optimization Algorithms (ACO) -- 7.7 Modi cations of the Ant Colony Optimization (ACO) Algorithm -- 7.8 Some Applications of Ant Colony Optimization -- 7.9 Summary -- 7.10 Problems -- 8 Physics Inspired Optimization Algorithms -- 8.1 Introduction -- 8.2 Simulated Annealing -- 8.3 Some Applications of Simulated Annealing -- 8.4 The Big Bang-Big Crunch Algorithm -- 8.5 Selected Numerical Studies Using the BB-BC Algorithm -- 8.6 Some Applications of Big Bang-Big Crunch Optimization -- 8.7 The Gravitational Search Algorithm -- 8.8 Selected Numerical Studies Using the GSA -- 8.9 Some Applications of the Gravitational Search Algorithm -- 8.10 Central Force Optimization -- 8.11 Selected Numerical Studies Using CFO -- 8.12 Some Applications of Central Force Optimization -- 8.13 Central Force Optimization Compared with GSA -- 8.14 Summary. , 8.15 Problems -- 9 Integer, Constrained and Multi-Objective Optimization -- 9.1 Integer Optimization -- 9.2 Constrained Optimization -- 9.3 Introduction to Multi-objective Optimization -- 9.4 Pareto Front -- 9.5 Methods for Solving the Multi-objective Optimization Problem -- 9.6 Summary -- 10 Recent Developments and Comparative Studies -- 10.1 Introduction -- 10.2 Other Nature Inspired Optimization Algorithms -- 10.3 Comparative Studies of Selected Methods on Speci c Test Problems -- 10.4 Suggestions for Further Studies -- 10.5 Summary -- 10.6 Problems -- A Test Functions -- A.1 Introduction -- A.2 Test Functions -- B Program Listings -- Solutions to Problems -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Chapter 7 -- Chapter 8 -- References -- Index -- Back Cover.

Language: English